Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains

• URL: http://arxiv.org/abs/2507.03026v1
• Date: Wed, 02 Jul 2025 21:33:48 GMT
• Title: Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
• Authors: Abhishek Verma, Nallarasan V, Balaraman Ravindran,
• Abstract summary: Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks.<n>This paper introduces the Generalized Adaptive Transfer Network (GATN), a deep RL architecture designed to tackle task generalization across domains.<n>GATN employs a domain-agnostic representation module, a robustness-aware policy adapter, and an efficient transfer scheduler to achieve these goals.
• Score: 11.705324423141606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks. While prior work, such as the Attend, Adapt, and Transfer (A2T) framework, addresses negative transfer and selective transfer, other critical challenges remain underexplored. This paper introduces the Generalized Adaptive Transfer Network (GATN), a deep RL architecture designed to tackle task generalization across domains, robustness to environmental changes, and computational efficiency in transfer. GATN employs a domain-agnostic representation module, a robustness-aware policy adapter, and an efficient transfer scheduler to achieve these goals. We evaluate GATN on diverse benchmarks, including Atari 2600, MuJoCo, and a custom chatbot dialogue environment, demonstrating superior performance in cross-domain generalization, resilience to dynamic environments, and reduced computational overhead compared to baselines. Our findings suggest GATN is a versatile framework for real-world RL applications, such as adaptive chatbots and robotic control.

Related papers

• On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning [0.0]
  We investigate the role of adversarial data augmentation (ADA) in enhancing both robustness and adaptivity in transfer learning settings.<n>We propose a unified framework that integrates ADA with consistency regularization and domain-invariant representation learning.<n>Our results highlight a constructive perspective of adversarial learning, transforming perturbation from a destructive attack into a regularizing force for cross-domain transferability.
  arXiv  Detail & Related papers  (2025-05-19T03:56:51Z)
• An advantage based policy transfer algorithm for reinforcement learning with measures of transferability [5.926203312586109]
  Reinforcement learning (RL) enables sequential decision-making in complex and high-dimensional environments.<n>This paper proposes an off-policy Advantage-based Policy Transfer algorithm, APT-RL, for fixed domain environments.
  arXiv  Detail & Related papers  (2023-11-12T04:25:53Z)
• Transfer RL via the Undo Maps Formalism [29.798971172941627]
  Transferring knowledge across domains is one of the most fundamental problems in machine learning. We propose TvD: transfer via distribution matching, a framework to transfer knowledge across interactive domains. We show this objective leads to a policy update scheme reminiscent of imitation learning, and derive an efficient algorithm to implement it.
  arXiv  Detail & Related papers  (2022-11-26T03:44:28Z)
• Safe Self-Refinement for Transformer-based Domain Adaptation [73.8480218879]
  Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. We propose a novel solution named SSRT (Safe Self-Refinement for Transformer-based domain adaptation), which brings improvement from two aspects.
  arXiv  Detail & Related papers  (2022-04-16T00:15:46Z)
• Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems [3.7311680121118345]
  Reinforcement learning (RL) has been used in a range of simulated real-world tasks. In this paper we investigate the impact of TL transfer parameters with fixed source and target roles.
  arXiv  Detail & Related papers  (2021-12-01T11:23:40Z)
• TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation [54.61786380919243]
  Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a labeled source domain to an unlabeled target domain. Previous work is mainly built upon convolutional neural networks (CNNs) to learn domain-invariant representations. With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge remains unexplored in the literature.
  arXiv  Detail & Related papers  (2021-08-12T22:37:43Z)
• Transformer-Based Source-Free Domain Adaptation [134.67078085569017]
  We study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. We propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA.
  arXiv  Detail & Related papers  (2021-05-28T23:06:26Z)
• OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations [6.730043708859326]
  We propose a transferability metric called Optimal Transport based Conditional Entropy (OTCE) OTCE characterizes transferability as a combination of domain difference and task difference, and explicitly evaluates them from data in a unified framework. Experiments on the largest cross-domain dataset DomainNet and Office31 demonstrate that OTCE shows an average of 21% gain in the correlation with the ground truth transfer accuracy.
  arXiv  Detail & Related papers  (2021-03-25T13:51:33Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)
• Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers [138.68213707587822]
  We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. We show that we can achieve this goal by compensating for the difference in dynamics by modifying the reward function. Our approach is applicable to domains with continuous states and actions and does not require learning an explicit model of the dynamics.
  arXiv  Detail & Related papers  (2020-06-24T17:47:37Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.